The field of the disclosure relates generally to signals corrupted by multipath, and, more particularly, to training a neural network for filtering a multipath corrupted signal.
Government agencies (e.g., DARPA) and other entities have shown interest in utilizing multipath exploitation in radar systems, rather than simply trying to eliminate such multipath from the radar return. In addition, communications signals have long had adaptive equalization filters to eliminate multipath from the received signal to enhance communications performance. In the case of radar systems, for example, an electromagnetic scattering and propagation ray tracing system may be used to create accurate electromagnetic models for urban sensing environments. Such systems provide better modeling than previous systems that performed radar height finding using multipath returns (a particular example of multipath exploitation). In addition, bistatic radar systems may be used for multipath exploitation with spatial diversity.
At least some known multipath filtering systems utilize neural networks. However, in at least some known systems, an iterated least square thresholding (ILST) algorithm used for non-linear parameter estimation of such multipath (typically this is caused by ground bounce) has some drawbacks. For example, the ILST algorithm is inherently a real-valued algorithm, and according cannot use complex phase information optimally. Further, the ILST algorithm struggles with convergence if the associated neural network has more than a small number of hidden layer neurons. These drawbacks lead to reduced performance when applied to more general applications of estimating radar or communications returns within urban clutter or other more complex multipath scenarios. In addition, these drawbacks limit applications to more complex and diverse systems, such as arrays, biomedical imaging, automotive anti-collision radars, and electronic warfare.
Further, many existing techniques for training real or complex neural networks rely on a backpropagation algorithm. These approaches suffer from convergence problems, especially for larger networks. To combat these problems and increase accuracy and decrease convergence time, backpropagation is usually preceded by some form of global optimization to be used as a starting point. However, such global optimization methods also suffer from convergence problems and can lead to inaccurate starting points for backpropagation, which then creates additional convergence problems. These convergence problems limit the size and complexity of the neural network architecture, and thus limit the complexity of the multipath scenario being modeled. These problems also affect accuracy of parameter estimation and signal classification.